Communications systems are characteristically susceptible to cross talk and other types of interference. The sources of such interference vary widely depending upon the environment in which particular communications systems are implemented and include, for example, other transceivers and communications systems. Interference can causes a decrease in the overall quality of a communications system, as indicated by the Signal-to-Noise Ratio (SNR) of a communications system. Consequently, improving the SNR will result in a corresponding improvement in the quality of the received signal. Fundamental theories, such as the Shannon Capacity Theorem, suggest that the capacity (amount of data which can be transferred error free) of a communications system is a function of the SNR.
Communications receivers typically process a received signal by sampling the received signal at a specified minimum sampling rate known as the Nyquist rate. The ability to monitor the interference affecting a communications system is limited by the sampling rate, which in conventional communications systems is the minimum sampling rate. Consequently, the degree to which the interference may be mitigated or compensated for is limited by the sampling rate of the receiver. By increasing the sampling rate, the effects of interference may be more optimally monitored and mitigated, thereby improving the SNR.
Based upon the foregoing, there is a need for an approach for processing data received from a communications channel to compensate for cross talk and other interference that does not suffer from the limitations of prior approaches.